Today, a huge amount of information is available in online documents such as web pages, newsgroup postings, and on-line news databases. Among the myriad types of information available, one useful type is the sentiment, or opinions, that people express towards a subject. A subject is either a topic of interest or a feature of the topic. For example, knowing the reputation of a company or its competitors' products or brands is valuable for product development, marketing and consumer relationship management. Traditionally, companies conduct consumer surveys for this purpose. Though well-designed surveys can provide quality estimations, they can be costly especially if a large volume of survey data is gathered. A technique to detect favorable and unfavorable opinions toward specific subjects, such as organizations and their products, within large numbers of documents offers enormous opportunities for various applications. It would provide powerful functionality for competitive analysis, marketing analysis, and detection of unfavorable rumors for risk management.
Thus there is a natural desire to detect and analyze favorability within online documents such as Web pages, chat rooms, and news articles, instead of making special surveys with questionnaires. Humans can easily recognize natural opinions among such online documents. In addition, it might be crucial to monitor such online documents, since they sometimes influence public opinion, and negative rumors circulating in online documents may cause critical problems for some organizations. However, analysis of favorable and unfavorable opinions is a task requiring high intelligence and deep understanding of the textual context, drawing on common sense and domain knowledge as well as linguistic knowledge. The interpretation of opinions can be debatable even for humans. For example, when we tried to determine if each specific document was on balance favorable or unfavorable toward a subject after reading an entire group of such documents, we often found it difficult to reach a consensus, even for very small groups of evaluators.
There has been extensive research on automatic text analysis for sentiment, such as the sentiment classifier described by B. Pang et al. in the paper “Thumbs Up? Sentiment Classification Using Machine Learning Techniques,” Proc. of the 2002 ACL EMNLP Conference, pages 79-86, 2002. Similarly, P. Subasic at al. discuss affect analysis in “Affect Analysis of Text Using Fuzzy Semantic Typing,” IEEE Trans. on Fuzzy Systems, Special Issue, August 2001. In the paper “Mining Product Reputations On The Web,” Proc. of the 8th ACM SIGKDD Conference, 2002, S. Morinaga et al. describe another method for extracting opinions. These methods only try to extract the overall opinion revealed in a document, either positive or negative, or somewhere in between.
Two challenging aspects of sentiment analysis are: frst, although the overall sentiment about a topic is useful, it is only a part of the information of interest. Document level sentiment classification fails to detect sentiment about individual aspects of the topic. In reality, for example, though one could be generally happy about his car, he might be dissatisfied by the engine noise. To the manufacturers, these individual weaknesses and strengths are equally important to know, or even more valuable than the overall satisfaction level of customers. Second, the association of the extracted sentiment to a specific topic is difficult. Most statistical opinion extraction algorithms perform poorly in this respect. An example of statistical opinion extraction is the ReviewSeer method described by K Dave et al. in “Mining The Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews,” Proc. of the 12th International WWW Conference, 2003. These algorithms either assume that the topic of the document is known a priori or simply associate the opinion to a topic term co-existing in the same context. The first approach requires a reliable topic or genre classifier that is a difficult problem in itself. A document (or even a portion of a document as small as a sentence) may discuss multiple topics and contain different opinions about different topics. For example, consider the following sentences from which ReviewSeer found positive opinions about the NR70 PDA produced by Sony:                1. As with every Sony PDA before it, the NR70 series is equipped with Sony's own Memory Stick expansion.        2. Unlike the more recent T series CLIEs, the NR70 does not require an add-on adapter for MP3 playback, which is certainly a welcome change.        3. The Memory Stick support in the NR70 series is well implemented and functional, although there is still a lack of nonmemory Memory Sticks for consumer consumption.        
The ReviewSeer statistical method, and most other statistical opinion extraction methods, would assign the same polarity to the Sony PDA and the T series CLI Es as that of NR70 for the first two sentences. That is incorrect for the T series CLIEs, although correct for the Sony PDA. The third sentence reveals a negative aspect of the NR70 (i.e., the lack of non-memory Memory Sticks) as well as a positive opinion in the primary phrase. These are expected shortcomings of the purely statistical approaches.
In addition, the prior art methods for extracting opinions typically analyze co-occurrences of expressions within a short distance or patterns to determine the relationships among expressions. Analysis of relationships based on distance has limitations. For example, even when a subject term and an opinion term are contained in the same sentence and located very close to each other, the subject term and the opinion term may not be related at all, as in the example “Although XXX is terrible, YYY is in fact excellent”, where “YYY” is not “terrible” at all. A major reason for the lack of focus on relationships between the opinion expressions and subjects may be due to the applications of these techniques. Many of these applications aim to classify the whole document as positive or negative toward a subject of the document that is specified either explicitly or implicitly. Furthermore, the subject of all of the opinion expressions are assumed to be the same as the document subject.
Therefore, there remains a need for a method of extracting opinions from text documents that take into consideration the opinions expressed in individual phrases and sentences rather than just the overall favorability or unfavorability opinions of the documents.